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Added api for getting/setting the kv_cache (#685)
The api provides access methods for retrieving the current memory buffer for the kv_cache and its token number. It also contains a method for setting the kv_cache from a memory buffer. This makes it possible to load/save history - maybe support --cache-prompt paramater as well? Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
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2 changed files with 44 additions and 0 deletions
27
llama.cpp
27
llama.cpp
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@ -1668,6 +1668,33 @@ int llama_model_quantize(
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return 0;
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}
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// Returns the KV cache that will contain the context for the
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// ongoing prediction with the model.
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const uint8_t * llama_get_kv_cache(struct llama_context * ctx) {
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return ctx->model.kv_self.buf.data();
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}
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// Returns the size of the KV cache
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size_t llama_get_kv_cache_size(struct llama_context * ctx) {
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return ctx->model.kv_self.buf.size();
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}
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int llama_get_kv_cache_token_count(struct llama_context * ctx) {
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return ctx->model.kv_self.n;
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}
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// Sets the KV cache containing the current context for the model
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void llama_set_kv_cache(
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struct llama_context * ctx,
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const uint8_t * kv_cache,
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size_t n_size,
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int n_token_count) {
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// Make sure we have the same kv cache setup
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LLAMA_ASSERT(ctx->model.kv_self.buf.size() == n_size);
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memcpy(ctx->model.kv_self.buf.data(), kv_cache, n_size);
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ctx->model.kv_self.n = n_token_count;
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}
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int llama_eval(
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struct llama_context * ctx,
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const llama_token * tokens,
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17
llama.h
17
llama.h
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@ -83,6 +83,23 @@ extern "C" {
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const char * fname_out,
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int itype);
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// Returns the KV cache that will contain the context for the
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// ongoing prediction with the model.
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LLAMA_API const uint8_t * llama_get_kv_cache(struct llama_context * ctx);
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// Returns the size of the KV cache
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LLAMA_API size_t llama_get_kv_cache_size(struct llama_context * ctx);
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// Returns the number of tokens in the KV cache
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LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
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// Sets the KV cache containing the current context for the model
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LLAMA_API void llama_set_kv_cache(
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struct llama_context * ctx,
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const uint8_t * kv_cache,
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size_t n_size,
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int n_token_count);
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// Run the llama inference to obtain the logits and probabilities for the next token.
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// tokens + n_tokens is the provided batch of new tokens to process
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// n_past is the number of tokens to use from previous eval calls
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